Pavement freezing depth estimation using hybrid deep-learning models

Abstract

Predicting pavement temperature by depth is crucial for road design, analysis, and maintenance. However, current methods predominantly utilize regression and/or open-form solutions focusing on highways. Additionally, most machine-learning models focus on asphalt layers and do not extend to deeper pavement layers. Therefore, this study provides deep-learning models using weather parameters to predict pavement temperature from surface to sublayers and estimate pavement freezing depth for developing massive apartment complexes. Temperature-by-depth data collected from thin pavements from three locations in South Korea were used. Comparative analyses of long short-term memory (LSTM), convolutional neural network-LSTM (CNNLSTM), and convolutional LSTM were performed. Results showed that CNN-LSTM model performed better with coefficients of determination (R2) of 0.965, 0.987, and 0.981. Additionally, the CNN-LSTM predicted freezing depth with 0.3%-13.1% error margins outperforming the LSTM, Aldrich's, and Korean Ministry of Transport approaches. The proposed approach shows that deep-learning models better estimate the freezing depth of pavements than existing approaches.